David Akomolafe 

Municipal Solid Waste Identification, Classification, and Valorization.

My research focuses on the intersection of process systems engineering and artificial intelligence to advance the circular economy. Specifically, I develop real-time algorithms that identify and classify materials for industrial processes. One of my application is the classification and function approximation models that leverage hyperspectral imaging (HSI) and machine learning to automate the sorting of municipal solid waste streams. 

Reinforcement Learning for Chemical Process Systems using a Hybrid Approach.

My research focuses on the intersection of process control, simulation, and dynamic optimization to leverage the power of reinforcement learning (RL) combined with traditional first-principles models to develop adaptive models for the dynamic control and optimization of complex, nonlinear chemical processes. 

• Akomolafe D, Klinger J, Saha N, Kuns M, Linck M, El Zahab Z, Bhattacharyya Z, “Real-Time Municipal Solid Waste Classification and Thermochemical Property Prediction Using Hyperspectral Imaging and Machine Learning” Paper 59b, AIChE Spring Meeting, Houston, TX, April 12-16, 2026

• Akomolafe D, Klinger J, Saha N, Kuns M, Linck M, El Zahab Z, Bhattacharyya Z, “Geometrical Sampling, Gaussian Mixture Modeling, and Spectral Feature Extraction for Classifying Municipal Solid Waste Using Hyperspectral Data”, Paper 713b, AIChE Annual Meeting, Boston, MA, November 2-6, 2025